Massachusetts ZIP Code Desirability Dashboard

Advanced Interactive Analysis • 493 ZIP Codes • ACS 2019-2023 5-Year Estimates
Average Score
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Overall desirability
Analyzed ZIPs
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After filtering
Median Income
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Regional average
Education Level
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Bachelor's degree+
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Top Performer
Analyzing data...
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Distribution Pattern
Analyzing data...
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Key Correlation
Analyzing data...
🎛️ Advanced Filtering System
Top 20 Performing ZIP Codes
Highest overall desirability scores
Bar Chart
Score Distribution Analysis
Frequency distribution of overall scores
Histogram
Geographic Heatmap: Overall Desirability Scores
Choropleth visualization showing composite scores across Massachusetts ZIP codes
Choropleth Map
Geographic Clustering: Performance Categories
K-Means clustering (k=4) revealing regional socioeconomic patterns
Cluster Map
Income-Score Correlation
Relationship between median income and desirability
Scatter Plot
Education-Score Correlation
Relationship between education and desirability
Scatter Plot
Dimension Score Heatmap: Comparative Analysis
Top 15 vs. Bottom 15 ZIP codes across all five performance dimensions
Heatmap
Performance Cluster Analysis
Average dimension scores by cluster group
Grouped Bar Chart
Multi-ZIP Comparison Tool
Interactive side-by-side comparison (select up to 5 ZIP codes)
Interactive Tool
Select ZIP Codes to Compare:
Comprehensive Data Table
Complete dataset with sortable columns • -- ZIP codes displayed
Interactive Table
ZIP Code Population Overall Score Median Income Education % Unemployment % Rent Burden % Cluster
📚 Methodology & Scoring Framework
The overall desirability score is computed as a weighted composite of five normalized dimension scores using min-max scaling methodology to ensure comparability across metrics.
25%
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Income Score
Min-max normalized median household income (S1901_C01_012E). Higher income receives higher scores, reflecting economic prosperity and purchasing power.
25%
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Education Score
Percentage of population aged 25+ with bachelor's degree or higher (S1501_C02_015E). Reflects human capital and educational attainment levels.
20%
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Employment Score
Inverse normalized unemployment rate for population 16+ in labor force (S2301_C04_001E). Lower unemployment equals higher score.
20%
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Housing Affordability Score
Inverse of rent burden - percentage of renter households paying ≥35% of income on rent (DP04_0142PE). Lower burden indicates better housing affordability.
10%
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Age Structure Score
Inverse of percentage population aged 65+ (DP05_0024PE). Accounts for demographic diversity and working-age population ratio.
📖 Data Sources & Citations (APA Format)
All data sourced from the U.S. Census Bureau's American Community Survey
U.S. Census Bureau. (2024). American Community Survey 2019–2023 5-year estimates, table DP04: Selected Housing Characteristics [Data set]. Retrieved from https://data.census.gov
U.S. Census Bureau. (2024). American Community Survey 2019–2023 5-year estimates, table DP05: ACS Demographic and Housing Estimates [Data set]. Retrieved from https://data.census.gov
U.S. Census Bureau. (2024). American Community Survey 2019–2023 5-year estimates, table S1501: Educational Attainment [Data set]. Retrieved from https://data.census.gov
U.S. Census Bureau. (2024). American Community Survey 2019–2023 5-year estimates, table S1901: Income in the Past 12 Months (in 2023 Inflation-Adjusted Dollars) [Data set]. Retrieved from https://data.census.gov
U.S. Census Bureau. (2024). American Community Survey 2019–2023 5-year estimates, table S2301: Employment Status [Data set]. Retrieved from https://data.census.gov
U.S. Census Bureau. (2023). 2020 Census 5-digit ZIP Code Tabulation Area (ZCTA5) TIGER/Line Shapefile [Shapefile]. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html
Project Information: This dashboard was developed for the Computerization and Visualization course at Northeastern University. The analysis employs advanced data science techniques including K-Means clustering, min-max normalization, and interactive geospatial visualization. Technologies used: Python (Pandas, NumPy, Scikit-learn, GeoPandas, Plotly), HTML5, CSS3, JavaScript, and D3.js.